research-article

Que2Search: Fast and Accurate Query and Document Understanding for Search at Facebook

Authors:

Kaushik Rangadurai,

Siddarth Malreddy,

Fedor BorisyukAuthors Info & Claims

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Pages 3376 - 3384

https://doi.org/10.1145/3447548.3467127

Published: 14 August 2021 Publication History

Abstract

In this paper, we present Que2Search, a deployed query and product understanding system for search. Que2Search leverages multi-task and multi-modal learning approaches to train query and product representations. We achieve over 5% absolute offline relevance improvement and over 4% online engagement gain over state-of-the-art Facebook product understanding system by combining the latest multilingual natural language understanding architectures like XLM and XLM-R with multi-modal fusion techniques. In this paper, we describe how we deploy XLM-based search query understanding model that runs <1.5ms @P99 on CPU at Facebook scale, which has been a significant challenge in the industry. We also describe what model optimizations worked (and what did not) based on numerous offline and online A/B experiments. We deploy Que2Search to Facebook Marketplace Search and share our deployment experience to production and tuning tricks to achieve higher efficiency in online A/B experiments. Que2Search has demonstrated gains in production applications and operates at Facebook scale.

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Cited By

Kekuda AZhang YUdayashankar A(2024)Embedding based retrieval for long tail search queries in ecommerceProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688039(771-774)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688039
Rossi NLin JLiu FYang ZLee TMagnani ALiao CSerra ESpezzano F(2024)Relevance Filtering for Embedding-based RetrievalProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680095(4828-4835)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680095
Lin JYadav SLiu FRossi NSuram PChembolu SChandran PMohapatra HLee TMagnani ALiao CSerra ESpezzano F(2024)Enhancing Relevance of Embedding-based Retrieval at WalmartProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680047(4694-4701)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680047
Show More Cited By

Index Terms

Que2Search: Fast and Accurate Query and Document Understanding for Search at Facebook
1. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval
  2. World Wide Web
    1. Web applications
      1. Electronic commerce
        Online shopping

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cover image ACM Conferences

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

August 2021

4259 pages

ISBN:9781450383325

DOI:10.1145/3447548

General Chairs:
Feida Zhu
Singapore Management University
,
Beng Chin Ooi
National University of Singapore
,
Chunyan Miao
Nanyang Technology University
,
Program Chairs:
Haixun Wang,
Iryna Skrypnyk,
Wynne Hsu,
Sanjay Chawla

Copyright © 2021 ACM.

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Published: 14 August 2021

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August 14 - 18, 2021

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Cited By

Kekuda AZhang YUdayashankar A(2024)Embedding based retrieval for long tail search queries in ecommerceProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688039(771-774)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688039
Rossi NLin JLiu FYang ZLee TMagnani ALiao CSerra ESpezzano F(2024)Relevance Filtering for Embedding-based RetrievalProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680095(4828-4835)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680095
Lin JYadav SLiu FRossi NSuram PChembolu SChandran PMohapatra HLee TMagnani ALiao CSerra ESpezzano F(2024)Enhancing Relevance of Embedding-based Retrieval at WalmartProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680047(4694-4701)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680047
Zhang YSong YLu SLiu QWang XSerra ESpezzano F(2024)VIER: Visual Imagination Enhanced Retrieval in Sponsored SearchProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680005(4293-4297)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680005
Agarwal PSK MPancha NHazra KXu JRosenberg CChua TNgo CKumar RLauw HKa-Wei Lee R(2024)OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest SearchCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648309(121-130)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648309
Yang JMagnani AYang B(2024)Disentangled Prompt Learning for Transferable, Multimodal, Few-Shot Image Classification2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825383(3343-3352)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825383
Zan WHan YJiang XXiao YYang YChen DChen SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)SPM: Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan SearchProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615500(4923-4929)Online publication date: 21-Oct-2023
https://doi.org/10.1145/3583780.3615500
He YTian YWang MChen FYu LTang MChen CZhang NKuang BPrakash A(2023)Que2Engage: Embedding-based Retrieval for Relevant and Engaging Products at Facebook MarketplaceCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3584633(386-390)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3584633
Wang WGuo YShen CDing SLiao GFu HPrabhakar PChen HDuh WHuang HKato MMothe JPoblete B(2023)Integrity and Junkiness Failure Handling for Embedding-based Retrieval: A Case Study in Social Network SearchProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591831(3250-3254)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591831
Wang WHu BPeng ZZhong MZhang ZLiu ZZhang GZhou J(2023)GARCIA: Powering Representations of Long-tail Query with Multi–granularity Contrastive Learning2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00244(3182-3195)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00244
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